BALANCED AWARE FIREFLY OPTIMIZATION BASED COST-EFFECTIVE PRIVACY PRESERVING APPROACH OF INTERMEDIATE DATA SETS OVER CLOUD COMPUTING

Cloud computing is an embryonic archetype with remarkable impetus; however its exclusive facets intensify safety and privacy confronts. In the previous method, the privacy of intermediate data set problems is dealt with which is concentrated to regain privacy sensitive information. Alternatively the previous system contains problem with time and cost intricacy. As well it contains issue with dealing privacy conscious well-organized scheduling of intermediate data sets in cloud by considering privacy preserving. In order to surmount the above stated problems, in the existing system, enhanced balanced scheduling methodology is presented to get better the cost complexity and privacy preservation. Balanced aware FireFly Optimization (BFFO) is used for proficient privacy conscious data set scheduling. This technique is utilized to discover the resolution that carries out best on poise amongst a set of resolutions with similar execution time. Consequently the research system gives superior privacy preservation and enhanced scheduling cost more willingly than the previous method. The encryption technique is used to guarantee the security and end users decrypted the real information with improved privacy. The experimentation outcome show that the presented method confirms superior privacy, lesser cost, lesser time complexity and proficient storage metrics utilizing BFFO methodology compared to the previous Cost based Heuristic (C_HEU) algorithm

Chaotic Quantum Double Delta Swarm Algorithm using Chebyshev Maps: Theoretical Foundations, Performance Analyses and Convergence Issues

Quantum Double Delta Swarm (QDDS) Algorithm is a new metaheuristic algorithm inspired by the convergence mechanism to the center of potential generated within a single well of a spatially co-located double-delta well setup. It mimics the wave nature of candidate positions in solution spaces and draws upon quantum mechanical interpretations much like other quantum-inspired computational intelligence paradigms. In this work, we introduce a Chebyshev map driven chaotic perturbation in the optimization phase of the algorithm to diversify weights placed on contemporary and historical, socially-optimal agents' solutions. We follow this up with a characterization of solution quality on a suite of 23 single-objective functions and carry out a comparative analysis with eight other related nature-inspired approaches. By comparing solution quality and successful runs over dynamic solution ranges, insights about the nature of convergence are obtained. A two-tailed t-test establishes the statistical significance of the solution data whereas Cohen's d and Hedge's g values provide a measure of effect sizes. We trace the trajectory of the fittest pseudo-agent over all function evaluations to comment on the dynamics of the system and prove that the proposed algorithm is theoretically globally convergent under the assumptions adopted for proofs of other closely-related random search algorithms.Comment: 27 pages, 4 figures, 19 table

Theoretical and practical convergence of a self-adaptive penalty algorithm for constrained global optimization

This paper proposes a self-adaptive penalty function and presents a penalty-based algorithm for solving nonsmooth and nonconvex constrained optimization problems. We prove that the general constrained optimization problem is equivalent to a bound constrained problem in the sense that they have the same global solutions. The global minimizer of the penalty function subject to a set of bound constraints may be obtained by a population-based meta-heuristic. Further, a hybrid self-adaptive penalty firefly algorithm, with a local intensification search, is designed, and its convergence analysis is established. The numerical experiments and a comparison with other penalty-based approaches show the effectiveness of the new self-adaptive penalty algorithm in solving constrained global optimization problems.The authors would like to thank the referees, the Associate Editor and the Editor-in-Chief for their valuable comments and suggestions to improve the paper. This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Funda¸c˜ao para a Ciˆencia e Tecnologia within the projects UID/CEC/00319/2013 and UID/MAT/00013/2013.info:eu-repo/semantics/publishedVersio

Multi self-adapting particle swarm optimization algorithm (MSAPSO).

The performance and stability of the Particle Swarm Optimization algorithm depends on parameters that are typically tuned manually or adapted based on knowledge from empirical parameter studies. Such parameter selection is ineffectual when faced with a broad range of problem types, which often hinders the adoption of PSO to real world problems. This dissertation develops a dynamic self-optimization approach for the respective parameters (inertia weight, social and cognition). The effects of self-adaption for the optimal balance between superior performance (convergence) and the robustness (divergence) of the algorithm with regard to both simple and complex benchmark functions is investigated. This work creates a swarm variant which is parameter-less, which means that it is virtually independent of the underlying examined problem type. As PSO variants always have the issue, that they can be stuck-in-local-optima, as second main topic the MSAPSO algorithm do have a highly flexible escape-lmin-strategy embedded, which works dimension-less. The MSAPSO algorithm outperforms other PSO variants and also other swarm inspired approaches such as Memetic Firefly algorithm with these two major algorithmic elements (parameter-less approach, dimension-less escape-lmin-strategy). The average performance increase in two dimensions is at least fifteen percent with regard to the compared swarm variants. In higher dimensions (≥ 250) the performance gain accumulates to about fifty percent in average. At the same time the error-proneness of MSAPSO is in average similar or even significant better when converging to the respective global optima’s

Optimal Clustering Framework for Hyperspectral Band Selection

Band selection, by choosing a set of representative bands in hyperspectral image (HSI), is an effective method to reduce the redundant information without compromising the original contents. Recently, various unsupervised band selection methods have been proposed, but most of them are based on approximation algorithms which can only obtain suboptimal solutions toward a specific objective function. This paper focuses on clustering-based band selection, and proposes a new framework to solve the above dilemma, claiming the following contributions: 1) An optimal clustering framework (OCF), which can obtain the optimal clustering result for a particular form of objective function under a reasonable constraint. 2) A rank on clusters strategy (RCS), which provides an effective criterion to select bands on existing clustering structure. 3) An automatic method to determine the number of the required bands, which can better evaluate the distinctive information produced by certain number of bands. In experiments, the proposed algorithm is compared to some state-of-the-art competitors. According to the experimental results, the proposed algorithm is robust and significantly outperform the other methods on various data sets

On PAPR Reduction of OFDM using Partial Transmit Sequence with Intelligent Optimization Algorithms

In recent time, the demand for multimedia data services over wireless links has grown up rapidly. Orthogonal Frequency Division Multiplexing (OFDM) forms the basis for all 3G and beyond wireless communication standards due to its efficient frequency utilization permitting near ideal data rate and ubiquitous coverage with high mobility. OFDM signals are prone to high peak-to-average-power ratio (PAPR). Unfortunately, the high PAPR inherent to OFDM signal envelopes occasionally drives high power amplifiers (HPAs) to operate in the nonlinear region of their characteristic leading out-of-band radiation, reduction in efficiency of communication system etc. A plethora of research has been devoted to reducing the performance degradation due to the PAPR problem inherent to OFDM systems. Advanced techniques such as partial transmit sequences (PTS) and selected mapping (SLM) have been considered most promising for PAPR reduction. Such techniques are seen to be efficient for distortion-less signal processing but suffer from computational complexity and often requires transmission of extra information in terms of several side information (SI) bits leading to loss in effective data rate. This thesis investigates the PAPR problem using Partial Transmit Sequence (PTS) scheme, where optimization is achieved with evolutionary bio-inspired metaheuristic stochastic algorithms. The phase factor optimization in PTS is used for PAPR reduction. At first, swarm intelligence based Firefly PTS (FF-PTS) algorithm is proposed which delivers improved PAPR performance with reduced searching complexity. Following this, Cuckoo Search based PTS (CS-PTS) technique is presented, which offers good PAPR performance in terms of solution quality and convergence speed. Lastly, Improved Harmony search based PTS (IHS-PTS) is introduced, which provides improved PAPR. The algorithm has simple structure with a very few parameters for larger PTS sub-blocks. The PAPR performance of the proposed technique with different parameters is also verified through extensive computer simulations. Furthermore, complexity analysis of algorithms demonstrates that the proposed schemes offer significant complexity reduction when compared to standard PAPR reduction techniques. Findings have been validated through extensive simulation tests

A Co-evolutionary, Nature-Inspired Algorithm for the Concurrent Generation of Alternatives

Engineering optimization problems usually contain multifaceted performance requirements that can be riddled with unquantifiable specifications and incompatible performance objectives. Such problems typically possess competing design requirements which are very difficult – if not impossible – to quantify and capture at the time of model formulation. There are invariably unmodelled design issues, not apparent at the time of model construction, which can greatly impact the acceptability of the model’s solutions. Consequently, when solving many “real life” mathematical programming applications, it is generally preferable to formulate several quantifiably good alternatives that provide very different perspectives to the problem. These alternatives should possess near-optimal objective measures with respect to all known modelled objective(s), but be fundamentally different from each other in terms of the system structures characterized by their decision variables. This solution approach is referred to as modelling-to-generate-alternatives (MGA). This study demonstrates how the nature-inspired, Firefly Algorithm can be used to concurrently create multiple solution alternatives that both satisfy required system performance criteria and yet are maximally different in their decision spaces. This new co-evolutionary approach is very computationally efficient, since it permits the concurrent generation of multiple, good solution alternatives in a single computational run rather than the multiple implementations required in previous MGA procedures